Attenuating attrition

High drug attrition rates from poor safety have spawned
numerous efforts to use in silico methods to improve molecule design,
but none of the algorithms created so far has emerged as a true game changer.
Now, AstraZeneca plc and Roche have concluded that
better prediction requires more data, and they are pooling their information
via an intermediary cheminformatics company, MedChemica Ltd., to produce a new
set of design rules.

In
1997, Christopher Lipinski created the first widely recognized set of rules to
guide the optimization of physicochemical properties, such as solubility and
lipophilicity, and facilitate the generation of drug-like compounds.

Since
then, computational chemists have created numerous algorithms to aid molecule
design, most of which have improved upon Lipinski's rule of five. Fewer
advances have been made that help medicinal chemists optimize biological
properties such as toxicity and ADME.

MedChemica,
created by three former AstraZeneca scientists, believes it can jump-start
progress by analyzing how changing a molecule's structure affects its behavior
in biological assays.

Although
other algorithms try to relate structure to biological function, most of the
analyses look at modifications across a wide array of diverse structures.
MedChemica's approach is to look at modifications in a set of similar
structures and see how minor differences affect the compounds' biological
activity.

Al
Dossetter, managing director of MedChemica, said the advantage of the company's
platform is the WizePairZ algorithm that looks at pairs of fragments that are
similar in structure but differ by a chemical group, such as a change from
chlorine to fluorine or the addition of a methyl group.

This
platform, he told SciBX, captures the chemical environment of the
fragment change. For example, it incorporates the fact that the effect of
changing chlorine to fluorine on a molecule will depend on the surrounding
structure. The result is a rule that is context dependent.

The
MedChemica approach applies to small molecules and uses only partial chemical
structures, thus keeping compound identities out of the picture.

Because
the platform does not reveal compound identities, AstraZeneca and Roche can
share knowledge without disclosing proprietary information.

By
collaborating to produce a new set of design rules, the pharmas are aiming to
reduce lead optimization time in drug discovery, during which compounds from
screening hits are modified to improve their pharmacokinetics and reduce their
potential for toxicity.

The
lead optimization process generally requires the synthesis of 500-1,000
molecules that differ by minor chemical modifications and undergo testing in a
battery of preclinical assays. With the aid of an improved set of rules, they
hope to reduce the number of compounds that need to be synthesized to reach the
optimal clinical candidate.

Although
each pharma has rich compound libraries with millions of molecules and large
amounts of experimental data, AstraZeneca and Roche believe that detecting
significant trends requires even greater statistical power, which will come
with consolidating their databases and increasing the number of matched pairs.

Dossetter
said smaller databases only allow researchers to extract one to five matched
pairs, which have a low fidelity of prediction. Ten matched pairs are
sufficient to draw a prediction, but reliability increases significantly with
20 matched pairs.

The
MedChemica database contains 1.2 million datapoints, each of which represents a
single molecule fragment in a single assay. It includes 31 different assays,
although more are likely to be added in the future, and not all molecules have
been tested in all assays.

Prior
to partnering with MedChemica, AstraZeneca and Roche each had large libraries
of compounds with corresponding experimental data. MedChemica is compiling
those data into a single integrated database.

The
proportion of the full MedChemica database contributed by each partner was not
disclosed. The Roche data includes molecules and results from studies at its Genentech Inc. unit.

Compatibility
of the datasets from Roche and AstraZeneca was key to forming the
collaboration. The two companies are in discussions with other big pharmas that
also may join the partnership and would need to provide complementary datasets.

Mike
Snowden, head of discovery sciences innovative medicines at AstraZeneca, said
that the compatibility requirements for joining are having a database with a
diverse compound background, large numbers of molecule pairs and biological
data that are produced by similar techniques to those of Roche and AstraZeneca.

"By
pooling datasets, we believe predictions of the platform will get better and
better," he told SciBX.

Each
partner will get a copy of the resulting database, dubbed the Grand Rule
Database. MedChemica also will offer molecule optimization consulting services
to nonpartners.

Snowden
is not concerned about losing a competitive advantage or leveling the playing
field. Although the rules can benefit all medicinal chemists, he said success
in drug development depends on having a good molecule at the starting point.

The
principal limitation of the collaborative effort may be that the assay data are
entirely based on in vitro and cell-based experiments. As yet, no in
vivo data either from animals or clinical trials have been included. Since in
vitro data often do not translate directly to results in the clinic, the
rules MedChemica derives may shorten the time to selecting a clinical candidate
but may not alter its chances of success in human trials.

Snowden acknowledged that the tool may have limitations but
said the goal is to learn how to predict the compounds that should not be made.

Access this BioCentury Innovations article article for your individual use via a permanent link that allows you to read or print the article: $30.
The article link will be posted on the purchase transaction web page, and also emailed to you with your purchase confirmation.

Purchase This Article for Limited One-Time Distribution and Posting to Your Website :

Receive a formatted PDF reprint of this article with rights for limited one-time redistribution and posting to your website: $750. Please allow 24-48 hours for delivery.

Purchase Options

Purchase this article for individual use $30 USDPurchase this article for limited one-time distribution and website posting $750 USD